Evidence map›Paper›PMID 41790163›Full record

ArticleNeuroradiology2026

Deep learning-based vessel segmentation in non-contrast black-blood MRI for treated aneurysm follow-up: a comparative study with TOF-MRA and DSA.

Samer Elsheikh, Alexander Rau, Petra Cimflova, Urs Würtemberger, Elias Kellner, Horst Urbach, Marco Reisert

Abstract readComparative Study
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Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Samer ElsheikhDepartment of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany. samer.elsheikh@uniklinik-freiburg.de.
Alexander RauDepartment of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany.
Petra CimflovaDepartment of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany.
Urs WürtembergerDepartment of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany.
Elias KellnerMedical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany.
Horst UrbachDepartment of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany.
Marco ReisertMedical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAutomated segmentation of intracranial vessels in 3D black-blood T1W-MRI is feasible. This study evaluates its utility for detecting recurrent aneurysms and compares automatically labeled MRI (AL-BB-MRI) with time-of-flight MRA (TOF-MRA) and digital subtraction angiography (DSA). MATERIALS (PATIENTS) AND

methodsFor model development, the basal intracranial arteries were manually labeled in 37 3D black-blood T1W-MRI examinations from 31 patients with previously endovascularly treated aneurysms. An independent, consecutive series of 84 patients with 90 aneurysms was assessed by three readers to identify recurrences in AL-BB-MRI and TOF-MRA. DSA served as the reference standard when available.

resultsThe model achieved adequate vessel segmentation (Dice = 0.81). Agreement between AL-BB-MRI and TOF-MRA was in 70/90 (78%) aneurysms. Major recurrence rates were similar: 16/90 (18%) aneurysms on AL-BB-MRI and in 14/90 (16%) aneurysms on TOF-MRA. Discrepancies were most common in anterior communicating aneurysms (seven seen only in AL-BB-MRI) and basilar artery aneurysms (five only in TOF-MRA). Compared to DSA (n = 22), AL-BB-MRI had similar accuracy (73% vs. 77%), sensitivity (62% each), and specificity (79% vs. 86%). AL-BB-MRI and TOF disagreed in nine aneurysms: AL-BB-MRI was false positive in three and false negative in two, while TOF-MRA was false positive in two and false negative in another two aneurysms.

conclusionsAL-BB-MRI showed high agreement with TOF-MRA in recurrence detection of aneurysms in the anterior circulation. Both modalities performed similarly against DSA. AL-BB-MRI was superior after flow-diverter or stent-assisted treatment but less reliable for intrasaccular flow disruption. (Level of Evidence: 5B). The study was registered in the German Clinical Trials Register (DRKS00014644) on 07.05.2018.

Indexed as

Angiography, Digital SubtractionCerebral AngiographyDeep LearningIntracranial AneurysmMagnetic Resonance AngiographyAdultAgedFemaleFollow-Up StudiesHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalMaleMiddle AgedRecurrenceReproducibility of ResultsAutomated vessel segmentationBlack-blood MRI (BB-MRI)Digital subtraction angiography (DSA)Endovascular treatment follow-upIntracranial aneurysm recurrenceTime-of-flight MRA (TOF-MRA)

Identifiers

PMID41790163
PMCPMC13139250

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.